Looking back at history we appreciate that just because something is legal does not mean it is ethical and vice versa. For example, from recent history Facebook may have satisfied legal requirements with Cambridge Analytica but I think most of us would say what Facebook did was unethical.

Of course any data legal versus data ethical decisions are not made in a vacuum. There are many changing factors and competing interests that blur the line. Some of these factors and interests being the quickly changing dichotomy of global views on data privacy rights, changing demographics and views of transparency and privacy, information and physical security, and quickly changing legal and regulatory environments. These items lead to significant questions businesses and governments are facing around data and how to use it.

This leaves a lot of questions.

How should organizations react?

Does it matter if these actions are for our own “good”?

What if these actions are for people or society’s own “good”?

Does it matter if governments or organizations are transparent?

Can the ends justify the means?

The answers to these questions are not always so black and white. However, some organizations like Microsoft are taking the long view in my opinion and encouraging the world to align with General Data Protection Regulation (GDPR) standards. While others strongly oppose changes in line with the protectionist data privacy rights that GDPR provides.

My personal belief and hope is that society and organizations will be radically transparent in how data is collected and used. This means communicating with people simply and clearly. Doing this is not only good for people but also good for companies and society at large. Consumers are shifting and they want ethical, transparent, and trustworthy organizations to support. This is especially true for Millennial and Gen Z.

If organizations are radically transparent and simple on how data is being collected and used, then it is up to us as citizens to take the next step. Stop supporting companies, people, and parties that don't align with our beliefs. Doing so will result in beneficial change. At the same time make sure not to get caught up in reacting to news stories without knowing the facts as too often sensationalized stories sell over boring explanations.

Now it is time for you to make a difference and not accept data legal to trump data ethics. If you are an individual contributor and believe your organization’s actions are unethical - leave! If you lead a team or organization that handles data - be transparent and communicate simply! If you are a manager that has a team member that does not respect the data in your stewardship - educate and transform him! If you are a politician that has a bill come before you that seeks to remove data transparency - vote no!

Data legal versus data ethics is not just a matter of opinion or of profit. It is a matter of value and culture. Which way will you steer your ship?

Dave Mathias and Matt Jesser started the Data Able podcast where they dive into data. Each week they cover the culture, knowledge and practices that successful organizations, leaders and individuals use to get value out of data.

In Episode 003 they discuss the exciting future of self service analytics. Give it a listen and subscribe to Data Able on your favorite podcast catcher.

This month the Twin Cities hosted Startup Week. One of the many great sessions was about the history of data visualization, delivered by Matt Dubay on behalf of the Twin Cities Data Visualization Group (TC Data Viz) (of which I’m an organizer). There was an amazing level of engagement and energy in the room and for the topic. As a side-note, I encourage you to check out the TC Data Viz group if you haven’t already. It’s a place where all are welcome - beginner or advanced - technical or business users – and our tools are agnostic, whether open source or proprietary software. We provide a fun and creative space to share ways to display data in our businesses and communities.

One of the main themes discussed during the session was around data literacy. One astute person noted that IT and self-service BI cannot drive the destiny of data literacy within organizations. Instead, it needs to be something solved by the business users themselves. Only then will self-service BI truly succeed. I couldn't agree more! Data fluency is an upcoming challenge that business leaders, managers, and individuals need to make time for, if they’re going to create data-savvy organizations.

In related news, the September 2018 McKinsey Quarterly published an article entitled "Why data culture matters". The whole article is great and highly recommended, but one key insight I want to touch on was that “Data culture is decision culture". The takeaway here is that organizations shouldn’t "… approach data analysis as a cool 'science experiment' or an exercise in amassing data for data's sake. The fundamental objective in collecting, analyzing, and deploying data is to make better decisions". One other thing I want to touch on is their call for the “democratization of data" and its importance in a data culture. From the article: “… get data in front of people and they get excited. But building cool experiments or imposing tools top-down doesn't cut it. To create a competitive advantage, stimulate demand for data from the grass roots."

Certainly, executive buy-in is important for resource allocation and overarching strategy, but executives don't make most decisions. Organizations succeed by the many decisions each employee, contractor, and customer make each day. Empowering and encouraging those stakeholders to get excited about data whether it is educational opportunities, competitions, data-for-good initiatives, or other ways to help invigorate and empower data culture at the grassroots level is essential.

So a little homework this week:

Executive: Identify a way to empower and encourage your organization to support a grassroots-level data culture. What change can you support and encourage at the grassroots level so that everyone not only wants data but needs data to survive?

Managers: Identify a way you can empower and encourage your team to support a grassroots-level data culture. What new decisions can you or your team harness new or existing data to make better decisions than you had before?

Experienced contributors: Identify how you can better use data to make better decisions and even demand data that had not been used before to make decisions? Further, how you can you help support newer contributors in this effort?

New contributors: Provide a fresh insight on how your organization can better encourage using data in roles. Your fresh perspective has a distinct advantage of seeing what could be as your are not encumbered by what is or was.

Now go and do your part changing the data culture at your organization!

Dave Mathias and Matt Jesser started the Data Able podcast where they dive into data. Each week they cover the culture, knowledge and practices that successful organizations, leaders and individuals use to get value out of data.

In the third episode they discuss data fluency and why it is important for individuals and organizations to embrace it. Give it a listen and subscribe to Data Able on your favorite podcast catcher.

Design thinking is an approach made famous by IDEO and Stanford’s d.school. The premise is everyone is creative and that the human should be at the center of design. It aims to provide a framework to design things that are desirable, feasible, and viable. The design thinking approach is a six-step process of framing a question, gathering inspiration, generating ideas, making ideas tangible, testing to learn, and sharing the story. Going through this process can be linear but often isn’t.

Now ask are data fluency and design thinking similar? The answer is definitively yes! Design thinking is a mentality of framing problems, generating ideas, testing ideas and telling stories. Data fluency stresses a similar process. Further, when determining desirable, feasible, and viable you certainly need to understand what the data indicates. Additionally, data fluency is always focused on the pain of people whether internal or external customers just like design thinking has the human in the center and their pain and needs.

The other thing design thinking and data fluency have in common is they are both geared at democratizing out their practices to everyone. Design thinking aims to put design in the hands of everyone while data fluency aims to put data science in the hands of everyone. This of course brings about fear by some in respective professions but it really brings about opportunity for all. This practice democratization solidifies importance and adoption. There will always be a place for those specially skilled in the respective design and data science arts, but it is time for basic practices and understandings of both to be adopted by all.

As the saying goes, “there are three kinds of lies: lies, damn lies, and statistics.” It was made famous by Mark Twain but attributed to British Prime Minister Benjamin Disraeli. While that saying is certainly old, it is as relevant today as it was then.

Why when this saying is stated, do we chuckle or nod our head? Well, it relates to us and each has witnessed many times untrue statistics being used in a way that is meant to gain support. Or, even truthful statistics may be cited but with the understanding that the public will interpret the statistic in a different way. For example, when we see mean being cited instead of the median in a situation that clearly will confuse the public and should have median cited.

We are humans after all and are busy and don’t have an opportunity to track down every statistic. Plus, like most people, we have a bias to believe things that support our opinion and disbelieve things that don’t support our opinion. This is not because we are evil but because of our tribal nature. It doesn’t help that peer-reviewed research studies are being shot down left and right for lack of replicability.

While we are busy people, my belief is that if everyone had a little more knowledge of statistics mixed with a dose data skepticism, then we would have a better and more productive professional and public debate. Maybe we start this path by having us each take a critical data thinking course which not only encompasses statistics but also encourages data skepticism. Not because I don’t want people to believe things cited blindly, but because I want us to be better able to understand and believe things cited. As a place to start yourself thinking more skeptically, try consuming information sources that don’t directly align with your beliefs and take a look at sources cited when people cite statistics.

Love to hear what you think about this and if you have seen courses at high school or earlier level doing this? Contact me and let me know or if you have any other insights, thoughts, or suggestions then I would love to hear them.

Dave Mathias and Matt Jesser started the Data Able podcast where they dive into data. Each week they cover the culture, knowledge and practices that successful organizations, leaders and individuals use to get value out of data.

In the first episode they introduce each other and why they called the podcast Data Able. Give it a listen and subscribe to Data Able on your favorite podcast catcher.

Data like any language is effective when others around you understand it and make decisions based on its meaning. But, data fluent doesn't mean being a data scientist. Instead it means "the ability to understand and use data effectively to inform decisions" according to Mandinach and Gummer. [1] One addition to this definition would be ability to communicate with data.

Leaders with data fluency whether team leaders, department directors, or senior executives benefit. These data fluent leaders ask questions like those below but more importantly are able to make data informed decisions.

What are key metrics that help me understand my customer's experience?

Am I hiring, rewarding, promoting and training my team members to be data fluent?

What data can I share with others to empower them to make the organization better?

Am I being a good data steward and ensuring proper data privacy and ethics are being utilized?

How can I use data to make our operations more efficient and effective?

Am I communicating with data appropriately to show the value our organization

What new data could I seek out or capture to bring more organizational value?

What percentage of employees have access to self service business intelligence and analytics and have been trained on it?

What metrics do we track to measure our employee experience?

What percentage of data we capture are we using to inform decisions?

I understand my NPS is in the top quartile, but what is driving this metric and what other metrics should I be monitoring to understand my customer satisfaction?

How are we developing new products and services based upon data from our customers?

Further, data fluent leaders are able to help their organizations have a data driven or data informed culture. Doing so will not only lead to more fulfilling environment and to great success.

There was a lively discussion on several fronts, but key takeaways were as follows:

Building relationships is key. Most information work takes teams and that means working with people. The more you build relationships the better chance to succeed as Nate mentioned.

Bring everything back to problem being solved. Data and analytics only serve a purpose if they solve problems. As Jack succinctly mentioned it is all about solving problems and bringing conversations back to those problems will help ensure success.

Trust is key. As Serena mentioned being a trusted advisor as an analyst and business partner alike is a must. Serena has the unique experience playing both roles in sales and sales enablement and building trust with both these hats has been essential to her success.

Rapid prototyping should be norm. Rapid prototyping is a must for dashboards and both to help ensure customer satisfaction and efficiency. These rapid prototypes can be done in a dashboard tool if a similar dataset available but just as nice it can be hand drawn on a whiteboard or paper.

In addition to these takeaways, there was a good discussion on the role of self-service business intelligence (BI) and how much autonomy the business should have and how much of it stays in the analyst, data science, or technology hands. There was mixed feeling here both on panel and in audience. Some companies have shown more success than others in distributing data fluency and technology into the business. However, there was agreement that tools are making it more able for end users to do more challenging problems.

One metaphor that seemed to resonate is treating self-service BI as a grocery store and not a treasure chest can help. As Nate described this the analyst, technology, or data science groups ensure that often used data has been made available with appropriate cleaning, integrity, and trust to business users. However, organizations need to ensure end users have proper training, tools, and help available so they can focus on conversations and insights while reducing the risk of invalid data models or technical debt.

There was a lot of overall agreement that data fluency is critical for organizations broadly and the language of data will be more easily picked up by some than others. But, to have a data-driven or data-informed culture at an organization requires your people to be data fluent.

This is a short summary of the great discussion that occurred, and all are welcome to attend the next TC Data Fluency MeetUp will be in July (date TBD). If you are an analyst or data scientist, then this is a great opportunity to bring one or more of your business partners to help further your relationship.

Thank you to Nate, Serena, Jack, and everyone that attended, and Tricia, Nate, and I hope to see you in July.

Earlier this month I had the opportunity to attend the She Talks Data MeetUp in the Twin Cities. This group’s goal is “[o]ur goal is to build a close-knit community of women (and men in support of women) who can come together to grow professionally and personally.” It was started as an offshoot of the She Talks Data group in Silicon Valley a few months ago locally by Serena Roberts and Laura Madsen.

Serena has said several times that this is not just for women. Plus, one of my friends, Karla Hillier, was presenting, so I thought great to support her and at same time attend this new group and learn.

As I told Serena beforehand, I was afraid attending and how I would be received and feel. But my fear quickly dissipated from the moment I walked in. Right from the start it was an engaging and welcoming environment, but I did feel something different.

As the first speaker Emma Denny, an employment law attorney, kicked off right from the start the room was riveted. There were questions related to workplace discrimination and sexual harassment. Emma talked about rights that people had in Minnesota. But, she also talked about the high thresholds that people face in these cases and difficulty in proving these cases. There were great tips such as telling people in writing when they felt harassed and literally spelling it out that you think it is because of gender or other protected class.

At one point, Emma asked how many people in the room had felt discriminated or harassed at work and nearly everyone’s hand was raised. I can say I felt bummed and really more angry. I felt angry that so many talented amazing people in our community have felt discrimination and harassment. I felt angry that so many of amazing people will likely face this more as their career continues. I felt angry that people often time creating those environments are oblivious that it is even occurring until it is too late or worse don’t care.

After the group I reflected what I could do. Yes, as a product person at heart my nature is when I see a problem I want to help find a solution. Of course, there is no single solution, but we can all help one action at a time whether in groups or at work to provide a more inclusive environment.

I encourage other men to respectfully participate in She Talks Data and other groups like these where appropriate and where welcomed. Not only as a sign of support, but also to be in a better position ourselves to be supportive when challenging situations with bosses, colleagues, employees, and clients will inevitably occur. After all we are people on this journey of life together with a finite amount of time, so let’s make the most of it and support each other through it.

Shout out to all the great people I met and good conversations I had. Special shout out to: Jen Roberts and Tricia Duncan that I had pleasure of meeting and sitting with; Serena Roberts and Laura Madsen for organizing group locally and continued leadership in community; and Emma Denny and Karla Hillier for sharing their knowledge and inspiring others.

Interested in learning more? Go to the April 4th She Talk Data MeetUp and catch April Seifert who will be one of the presenters. In fact, April and I were just talking this morning on all things CX, analytics, and podcasting and sure she will have a lot of great wisdom to share.